A Pytorch implementation of the multi agent deep deterministic policy gradients (MADDPG) algorithm
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Updated
Apr 8, 2021 - Python
A Pytorch implementation of the multi agent deep deterministic policy gradients (MADDPG) algorithm
Deep Reinforcement Learning for Trading
Actor Critic using Kronecker-Factored Trust Region
Solving CartPole-v1 environment in Keras with Actor Critic algorithm an Deep Reinforcement Learning algorithm
Official repository for the paper "Exploring the Promise and Limits of Real-Time Recurrent Learning" (ICLR 2024)
Design and training of an RL agent to control a Quadcopter using Actor-Critic RL method
a collection of python notebooks using RL agents to play Atari games in OpenAI gym environments
Topics in Machine Learning @ IIIT Hyderabad (Fall 2021)
Deep Reinforcement Learning: Continuous Control. Solve the Unity ML-Agents Reacher Environment.
This repository contains high quality and tested implementation of Asynchronous Actor Critic Algorithm
Objective Stimuli Active Repeater
Implementations of some of the most well known Deep Reinforcement Learning algorithms
Fun with Reinforcement Learning in my spare time
Programming Assignments for Reinforcement Learning Specialization
Deep Reinforcement Learning: On-Policy Actor Critic methods. An implementation of Advantage Actor-Critic (A2C) and Proximal Policy Optimization (PPO) on the PyTorch Lightning framework.
A model to control a double-jointed arm to reach target using Deep Deterministic Policy Gradients
Implemented the Actor-Critic method using TensorFlow to train an agent on the Open AI Gym CartPole-V0 environment.
unRL (AKA "unreal") is a set of libraries providing Reinforcement Learning algorithms implemented in PyTorch or Jax.
Example VPG implementation with ReLAx
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